code: def test_create_keras_inputs_sparse_features(self):
      context_feature_columns = {
          "query": tf.feature_column.categorical_column_with_vocabulary_list(
              "query",
              vocabulary_list=[
                  "ranking",
                  "regression",
                  "classification",
                  "ordinal",
              ],
          )
      }
      example_feature_columns = {
          "title": tf.feature_column.categorical_column_with_vocabulary_list(
              "title",
              vocabulary_list=[
                  "ranking",
                  "regression",
                  "classification",
                  "ordinal",
              ],
          )
      }
      keras_inputs = feature.create_keras_inputs(
          context_feature_columns=context_feature_columns,
          example_feature_columns=example_feature_columns,
          size_feature_name="example_list_size",
      )
  
      self.assertCountEqual(
          keras_inputs.keys(),
          list(context_feature_columns.keys())
          + list(example_feature_columns.keys())
          + ["example_list_size"],
      )
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import six
  import tensorflow.compat.v2
  from tensorflow_ranking.python.keras import feature
label_desc: Use image node_id 5 for calling the EncodeListwiseFeaturesTest obj's underlying member method code with example usage: obj.test_create_keras_inputs_sparse_features() without return types